Can HelpingAI2 6B run on NVIDIA A100 80GB?

YES — Runs Great

C45Usable

Estimated from fit model

HelpingAI2 6B needs ~13.6 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~84 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 13.6 GB, 84.0 tok/s, Runs well

13.6 GB required80.0 GB available

17% VRAM used

Fit status

Runs well

Decode

84.0 tok/s

TTFT

2305 ms

Safe context

1.5M

Memory

13.6 GB / 80.0 GB

Memory breakdown

Weights3.7 GB

KV Cache0.7 GB

Runtime1.2 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsHelpingAI2 6B on NVIDIA A100 80GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 84.0 tok/s decode · 2.3s TTFT (warm) · 210 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	84.0 tok/s	1257 ms	1.5M
Coding	C	Runs well	84.0 tok/s	2305 ms	1.5M
Agentic Coding	C	Runs well	84.0 tok/s	3352 ms	1.5M
Reasoning	C	Runs well	84.0 tok/s	2724 ms	1.5M
RAG	C	Runs well	84.0 tok/s	4190 ms	1.5M

Quantization options

How HelpingAI2 6B (6B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.3 GB	Low	D39
Q3_K_S	3	2.9 GB	Low	D39
NVFP4	4

Get started

Copy-paste commands to run HelpingAI2 6B on your machine.

Run

lms load hf-helpingai--helpingai2-6b && lms server start

Upgrade options

Hardware that runs HelpingAI2 6B well

MacBook Pro M3 Max 128GBBudget pick

128 GB Unified (+48)

This setup is broadly balanced for this model.65.6 tok/s decode

~$2,499 MSRP

Mac Studio M2 Ultra 128GBBest value

128 GB Unified (+48)

This setup is broadly balanced for this model.84 tok/s decode

~$3,999 MSRP

Frequently asked questions

Can NVIDIA A100 80GB run HelpingAI2 6B?

Yes, NVIDIA A100 80GB can run HelpingAI2 6B with a C grade (Runs well). Expected decode speed: 84.0 tok/s.

How much VRAM does HelpingAI2 6B need?

HelpingAI2 6B (6B parameters) requires approximately 13.6 GB of memory with Q4_K_M quantization.

What is the best quantization for HelpingAI2 6B?

The recommended quantization for HelpingAI2 6B is Q4_K_M, which balances quality and memory efficiency.

What speed will HelpingAI2 6B run at on NVIDIA A100 80GB?

On NVIDIA A100 80GB, HelpingAI2 6B achieves approximately 84.0 tokens per second decode speed with a time-to-first-token of 2305ms using Q4_K_M quantization.

Can NVIDIA A100 80GB run HelpingAI2 6B for coding?

For coding workloads, HelpingAI2 6B on NVIDIA A100 80GB receives a C grade with 84.0 tok/s and 1.5M context.

What context window can HelpingAI2 6B use on NVIDIA A100 80GB?

On NVIDIA A100 80GB, HelpingAI2 6B can safely use up to 1.5M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA A100 80GB See all hardware for HelpingAI2 6B

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